Islet-Specific Expression of IL-10 Promotes Diabetes in Nonobese Diabetic Mice Independent of Fas, Perforin, TNF Receptor-1, and TNF Receptor-2 Molecules
Bibliographic record
Abstract
Several death-signaling or death-inducing molecules have been implicated in beta cell destruction, including Fas, perforin, and TNFR-1. In this study, we examined the role of each death-signaling molecule in the IL-10-accelerated diabetes of nonobese diabetic (NOD) mice. Groups of IL-10-NOD mice, each deficient in either Fas, perforin, or TNFR-1 molecules, readily developed insulitis, and subsequently succumbed to diabetes with an accelerated kinetics and incidence similar to that observed in their wild-type or heterozygous IL-10-NOD littermates. Similarly, a TNFR-2 deficiency did not block accelerated diabetes in IL-10-NOD mice and spontaneous diabetes in NOD mice. These results demonstrate that pancreatic IL-10 promotes diabetes independent of Fas, perforin, TNFR-1, and TNFR-2 molecules. Subsequently, when cyclophosphamide, a diabetes-inducing agent, was injected into insulitis-free NOD. lpr/lpr mice, none of these mice developed insulitis or diabetes. Our data suggest that cyclophosphamide- but not IL-10-induced diabetes is Fas dependent. Overall, these findings provide evidence that pancreatic expression of IL-10 promotes diabetes independent of the major death pathways and provide impetus for identification of novel death pathways precipitating autoimmune destruction of insulin-producing beta cells.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".